The present invention relates to an adaptive prediction differential PCM-type transmission process and apparatus with shaping of the quantization noise and which is used in telecommunications, particularly in telephony.
The PCM procedure (pulse code modulation) is widely used in the field of telecommunications and in particular in telephone transmissions. In this procedure on transmission, the signal to be transmitted is sampled, the samples obtained quantized, the quantized signals are coded in digital form and the coded signals transmitted, whilst on reception the signals received are decoded and the original signal restored.
An improvement to this procedure is obtained by replacing the quantization of the input signal by quantizing the difference between said signal and a prediction signal obtained from the formation of this difference. The prediction signal is supplied by a prediction circuit or predictor. This system is called differential PCM or DPCM for short.
Another improvement is obtained by multiplying the difference signal by a gain factor in order to utilize in an optimum manner the available levels of the quantizer. The quantizer signal is then divided by the same factor for restoring the original quantized sample.
In a differential PCM system, the prediction circuit is generally constituted by a linear filter which, on the basis of a sample sequence preceding the sample to be processed is able to provide a prediction relative to this sample.
Such a prediction filter can be fixed once and for all and in this case its characteristics are chosen so that it is adapted to the mean longterm spectrum of the signal to be transmitted. However, such a filter does not make is possible to obtain a very good transmission quality. The latter can be improved on adapting the prediction filter to the developments of the signal by periodically updating its characteristics.
This adaptation can be carried out sequentially or recursively by correcting at each sampling moment the characteristics of the filter as a function of the value taken by the difference signal at this moment. The adaptation criterion is that the average power of the difference signal (which is to some extent an error signal) is as low as possible.
This type of coding, called "adaptive prediction differential PCM" (or ADPCM for short) apply to the telephone speech signal and has already been described in a number of articles. The following articles provide a survey of this and describe certain adaptive quantization processes:
"Digital Coding of Speech Waveforms: PCM, DPCM and DM quantizers," by N. S. JAYANT, published in the U.S. Journal "Proceedings of IEEE," May 1974; PA1 "Adaptive predictive coding of speech signals" by B. S. ATAL and M. R. SCHROEDER, published in the U.S. Journal "B.S.T.J," Vol.49, October 1970; PA1 "Speech Coding" by J. L. FLANAGAN, M. SCHROEDER, B. ATAL R. CROCHIERE, N. S. JAYANT, J. M. TRIBOLET, published in the U.S. Journal IEEE-COM 27, No.4, April 1979; PA1 "Bit rate reduction by automatic adaptation of quantizer step size in DPCM systems" by P. CASTELLINO, G. MODENA, L. NEBBIA and C. SCAGLIOLA, published in the reports of the International Seminar on digital transmissions, Zurich 1974; PA1 "A robust adaptive quantizer" by D. J. GOODMAN and R. M. WILKINSON, published in the U.S. Journal IEEE Transactions on Communications, November 1975. PA1 (1) Design of more complex filters or the use of very high performance adaptive algorithms (recursive algorithms of the high-speed Kalman filter type or of the adaptive lattice filter type). Examples of work in this connection are provided by the following articles: PA1 (2) Shaping the quantization noise by prefiltering in accordance with the following principle: in conventional ADPCM coding, the quantization noise spectrum is in a first approximation roughly flat, which sometimes has the effect of making it clearly perceptible in the frequency ranges where the speech signal to be coded has a low power. Therefore, various devices have been designed in connection with coding by blocks with a medium and low bit rate (below 16 kbit/s) for shaping the quantization noise spectrum so as to eliminate this disadvantage. Such devices are described in the following articles: PA1 (b) the unquantized error signal e.sub.t is filtered by multiplying P successive samples of this signal, i.e. e.sub.t, e.sub.t-1, . . . , e.sub.t-P+1, in P coefficients equal respectively to said coefficients B1.sub.t, B2.sub.t, . . . , BP.sub.t and the products obtained are added, which supplies a filtered signal pe.sub.t, then on the basis of said signal pe.sub.t and the signal pe.sub.t obtained previously by filtering e.sub.t a signal pMA.sub.t is formed which is equal to .gamma..sub.MA pe.sub.t +(1-.gamma..sub.MA)pe.sub.t, in which .gamma..sub.MA is a regulatable coefficient between 0 and 1 (terminals included), the coefficients .gamma..sub.AR and .gamma..sub.MA not being simultaneously zero; said operations (a) and (b) being followed by an addition of the signals pAR.sub.t and pMA.sub.t, then by a delay of one sampling time of the sum obtained, which supplies the said prediction signal p.sub.t. PA1 (A) at least one of the following circuits: PA1 (B) an adder with two inputs connected to the outputs of the first and second algebraic circuits and with an output supplying a signal pAR.sub.t +pMA.sub.t ; PA1 (C) a circuit with a delay of one sampling period with an input connected to the output of the adder and an output supplying the said prediction signal p.sub.t. PA1 J. MAKHOUL and R. VISWANATHAN: "Adaptive lattice methods for linear prediction;" IEEE ASSP (Tulsa) 1978; PA1 J. MAKHOUL: "Stable and efficient lattice methods for linear prediction" published in the Journal IEEE Transactions on Acoustics, Speech and Signal Processing (ASSP), October 1977.
Systems using these general principles are briefly described by means of FIGS. 1 and 2. The circuit of FIG. 1 is a coding circuit and that of FIG. 2 a decoding circuit.
The ADPCM code of FIG. 1 comprises an algebraic subtracter 1 with two inputs, the first receiving the signal to be coded y.sub.t and the second a prediction signal p.sub.t. The output of this subtracter supplies a difference or error signal e.sub.t, which is applied to the input of an arithmetic unit 2 controlled by a signal e.sub.t-1. The output of arithmetic unit 2 supplies a signal en.sub.t which is applied to the input of a coding circuit 3. The output of this coder supplies a coded signal c.sub.t, which is applied on the one hand to a transmission channel and on the other to the input of a decoding--quantizing circuit 4. The latter supplies a signal en.sub.t, which is applied to the input of an arithmetic unit 5, controlled by signal e.sub.t-1. The output of this unit supplies a signal e.sub.t, which is a restored error signal, which is applied to a first input of an adaptive prediction circuit 8, to the first input of an algebraic adder 7 and finally to the input of a register 6 controlled in reading by pulses from a clock H. This register supplies a delayed signal e.sub.t-1, which is applied to the control inputs of circuits 2 and 5. The output of the adaptive prediction circuit 8 supplies the signal p.sub.t, applied on the one hand to the second input of subtracting circuit 1 and on the other hand to the second input of adding circuit 7, whose output supplies a restored signal y.sub.t, which is applied to a second input of circuit 8.
In the illustrated embodiment, speech signal y.sub.t is assumed to be applied to the input in digital form, e.g. in the form of a 12 bit linearized code supplied by the decompression of the standard PCM code (the coder and linearizer are not shown).
The system of circuits 2, 3, 4, 5, 6 constitutes an embodiment of a conventional adaptive quantizer, circuits 2 and 5 respectively having the functions of standardizing to a fixed value the power of error signal e.sub.t and restoring the true power to the quantized standardized signal en.sub.t in order to obtain the quantized error signal e.sub.t.
The function of register 6 is to make the value of signal e.sub.t-1 used for control purposes available at the desired time.
The ADPCM decoder of FIG. 2 comprises a decoding--quantizing circuit 4, whose input receives signal c.sub.t from the transmission channel and whose output supplies a signal en.sub.t, which is applied to the input of an arithmetic unit 5, controlled by a signal e.sub.t-1. The output of this unit supplies a signal e.sub.t, which is applied to a first input of an adaptive prediction circuit 8, to a first input of an algebraic adder 7 and finally to the input of a register 6 controlled in reading by a clock H, the output of said register being connected to the control input of circuit 5. The output of the adaptive prediction circuit 8 supplies a prediction signal p.sub.t, which is applied to a second input of algebraic adder 7, whose output supplies a signal y.sub.t. This signal is applied to a second input of circuit 8 and constitutes at the same time the output signal of the decoder, i.e. in definitive terms the transmitted signal corresponding to the input signal y.sub.t applied to the coder.
Circuits 4, 5, 6, 7 and 8 of this decoder are identical to the circuits with the same references of the coder of FIG. 1.
The present invention relates solely to the prediction circuit 8, whilst the other components can be of a per se known type.
In the field of the digital transmission of a telephone speech signal, compared with the standardized system operating at 64 kbit/s it is intended to pass to a lower bit rate of 32 kbit/s. Equipment making possible this reduction in the bit rate have been constructed in accordance with the aforementioned and known ADPCM process with a relatively good quality of the restored speech signal. However, for this application it is advantageous to further improve the quality obtained by a more complex coding. For other applications, it may be advantageous to reduce the rate to the minimum possible value in accordance with differential coding processes, whilst retaining a given quality:
A large amount of research has been carried out on adaptive predictors in this connection and they have followed two fundamental directions, as indicated below:
J. D. MARKEL and A. H. GRAY: "On correlation equation as applied to speech analysis" published in the U.S. Journal IEEE-Audio-Electroacoustic, AU-21, No.2 1973. PA2 J. MAKHOUL: "A class of all-zero lattice digital filters; properties and applications," published in the U.S. Journal IEEE-ASPP 26, No.4 1978. PA2 D. L. COHN and James L. MELSA: "The residual encoder--An improved ADPCM system for speech digitization," published in the Journal IEEE transactions on communications, September 1975. PA2 T. W. CAIRNS, W. A. COBERLY, D. F. FINDLEY: "ARMA modeling applied to linear prediction of speech," published in the Journal IEEE-ASSP, July 1978. PA2 C. SCAGLIOLA: "Automatic vocal tract parameter estimation by an iterative algorithm," published in the Italian Journal CSELT Rapporti tecnici, No.2, June 1975, pp.19 to 24. PA2 M. BEROUTI and J. MAKHOUL: "High quality adaptative prediction coding of speech," published in IEEE-ASSP, TULSA 1978. PA2 J. MAKHOUL, M. BEROUTI: "Adaptive noise spectral shaping and entropy coding in predictive coding of speech," published in IEEE Transactions on ASSP, February 1979. PA2 B. S. ATAL, M. R. SCHROEDER: "predictive coding of speech signals and subjective error criteria," published in IEEE Transactions on ASSP, June 1979. PA2 J. MENEZ, D. MAUDUIT: "Systeme de codage du signal de parole par decomposition spectrale" (Speech signal coding system by spectral decomposition), National Conference of Signal Processing and its applications, April 1977. PA2 J. E. STJERNVALL: "On rate and frequency allocation in subband of gaussian sources," Department of electrical engineering, Linkoping University SWEDEN. PA2 (a) a first circuit constituted by a filter of signal y.sub.t comprising a first group of N multipliers with two inputs respectively receiving N successive samples of y.sub.t, i.e. y.sub.t, y.sub.t-1, . . . , y.sub.t-N+1 (or derived samples) and N coefficients respectively equal to said coefficients A1.sub.t, A2.sub.t, . . . , AN.sub.t sampled in the first filter of the predictor acting on y.sub.t and an adding circuit with N inputs connected to the N multipliers of the first group and with an output supplying a filtered signal py.sub.t and and by a first algebraic circuit with two inputs, one connected to the output of the filter of y.sub.t and receiving the signal py.sub.t and the other connected to the output of the filter y.sub.t and receiving the signal py.sub.t, said first algebraic circuit supplying at one output a signal pAR.sub.t equal to .gamma..sub.AR py.sub.t +(1-.gamma..sub.AR) py.sub.t, in which .gamma..sub.AR is a regulatable coefficient between 0 and 1 (terminals included); PA2 (b) a second circuit constituted by a filter of the unquantized error signal e.sub.t comprising a second group of P multipliers with two inputs respectively receiving P successive samples of e.sub.t, i.e. e.sub.t, e.sub.t-1, . . . , e.sub.t-P+1 and P coefficients equal then respectively to said coefficients B1.sub.t, B2.sub.t, . . . , BP.sub.t sampled in the second filter of the predictor acting on e.sub.t and an adder with P inputs connected to P multipliers of the second group and with an output supplying a filtered signal pe.sub.t and by a second algebraic circuit with two inputs, one connected to the output of the filter of e.sub.t and receiving the signal pe.sub.t and the other to the output of the filter of e.sub.t and receiving the signal pe.sub.t, said second algebraic circuit supplying at an output a signal pMA.sub.t equal to .gamma..sub.MA pe.sub.t +(1-.gamma..sub.MA)pe.sub.t, in which .gamma..sub.MA is a regulatable coefficient between 0 and 1 (terminals included), the coefficients .gamma..sub.MA and .gamma..sub.AR not being simultaneously zero;
These articles descrlbe a procedure in which coding takes place by sample blocks and in the first two articles the prefilter is obtained by reversing the prediction filter, which is difficult to perform. Reference can also be made to the following articles:
These articles describe a procedure using subband analysis followed by coding in each band.
Like these earlier studies, the present invention is directed at the improvement of ADPCM coding equipment, particularly for high bit rates. It achieves this objective by means of an adaptive prediction circuit permitting an appropriate shaping of the quantization noise.
Prior to the definition of the invention, it is advantageous to define the terminology used. As in the description of the prior art provided hereinbefore, the restored signals on the basis of which the prediction is made, are in the form of a symbol surmounted by a bar, i.e. y.sub.t for the signal and e.sub.t for the quantized error, t indicating the processing time or the rank of the processed sample.
The quantization noise on the signal at the time t is called .DELTA.y.sub.t. It is equal to the difference between the restored signal y.sub.t and the incident signal y.sub.t. In the same way, the quantization noise on the prediction error is called .DELTA.e.sub.t and, at the same time t, it is equal to the difference between the restored error e.sub.t and the true error commited e.sub.t. Thus, we obtain by definition: EQU .DELTA.y.sub.t =y.sub.t -y.sub.t ( 1) EQU .DELTA.e.sub.t =e.sub.t -e.sub.t ( 2)
As stated hereinbefore, the quantization noise .DELTA.e.sub.t on the prediction error generally has an almost flat spectrum. The shaping of the quantization noise .DELTA.y.sub.t on the signal, which is the objective of the invention, consists of giving this noise a spectrum which is substantially of the same shape as the spectrum of signal y.sub.t, it then being stated that the spectra having been made "parallel". In this way, in the ranges where the signal y.sub.t is weak, the quantization noise is also weak and in the ranges where y.sub.t is strong, the noise assumes relatively high values without this being prejudicial to the transmission quality.
According to the invention, this shaping of the quantization noise is carried out within the scope of a prediction procedure by linear filter with recursive readjustment and not within the scope of a coding system by sample blocks, whereof a first disadvantage has been referred to hereinbefore and whereof a second disadvantage is the necessity of transmitting to the receiver the coefficients of the prediction filter calculated in the transmitter, in addition to the quantized error signal. This point will now be discussed in greater detail to facilitate the understanding of the invention.
In an adaptive prediction system with recursive readjustment the restored signal y.sub.t is equal to the sum of the predicted signal p.sub.t and the restored error e.sub.t (cf FIG. 1): EQU y.sub.t =p.sub.t +e.sub.t ( 3)
The predicted signal can be obtained by a linear operation of form: ##EQU1## which is equivalent to a filtering operation. Thus, N coefficients Al.sub.t,A2.sub.t . . . AN.sub.t are obtained which effect N samples, which are samples of the restored signal, namely: y.sub.t, y.sub.t-1, . . . , y.sub.t-N+1. We obtain from (3) and (4): ##EQU2##
On writing the equation (5) in the form of a "z" transform (where the variable z is equal to e.sup.j T, T being the sampling period), we obtain between y.sub.t and e.sub.t the following equation: ##EQU3##
The proportionality coefficient between y(z) and e(z) is a transfer function only having poles (which are the zeros of the denominator) and no zero (the numerator is equal to 1). The filter which performs this transfer function is called an all-pole filter.
The modeling of the signal described by the equations (3) to (6) is well known and is called autoregressive or AR for short, the term regressive indicating the recurring character of the process using a sequence of previously processed samples.
However, the predicted signal can also be obtained by a linear equation which is more complex than equation (4) by using the restored error signals e.sub.t-k in addition to y.sub.t-k in accordance with the linear equation: ##EQU4## the second summation corresponding to a prediction relative to the error signal pe.sub.t. Thus, P other coefficients B1.sub.t, B2.sub.t, . . . , BP.sub.t which affect the samples of the restored error, namely e.sub.t, e.sub.t-1, . . . , e.sub.t-P+1.
From (3) and (7) is obtained the equation: ##EQU5## and from this, by the same "z" transformation as hereinbefore, the equation between y(z) and e(z): ##EQU6##
This leads to a new transfer function not only having poles (the zeros of the denominator) but also zeros, which are those of the numerator. The filter performing this transfer function is called a pole-zero filter.
The modeling described by equations (3) and (7) to (9) is well known and is called "adjusted mean autoregressive" or AMAR for short. Modeling only involving the use of the numerator of the transfer function (9) is obtained by an all-zero filter and will be of the adjusted mean or AM type.
The present invention relates to the AMAR modeling of restored signals y.sub.t+1 and e.sub.t+1 which uses two series of coefficients, one A1.sub.t, A2.sub.t, . . . , AN.sub.t affecting N samples y.sub.t, y.sub.t-1, . . . , y.sub.t-N+1 of the restored signal and the other B1.sub.t, B2.sub.t, . . . , BN.sub.t affecting P samples e.sub.t, e.sub.t-1, . . . , e.sub.t-P+1 of the restored error. All these coefficients are readjusted at each time t (sequentially or recursively) in such a way that the mean power of the prediction error signal is minimal.
The context of the invention having already been described, a more specific description thereof will now be provided.
Instead of carrying out the prediction on the basis of signals y.sub.t and e.sub.t only (as in AMAR modeling), according to the invention use is also made of the real signal y.sub.t and the real error e.sub.t. For this purpose, there is on the one hand a linear filtering of y.sub.t using the coefficients A1.sub.t, A2.sub.t, . . . , AN.sub.t of the conventional modeling of y.sub.t+1, these coefficients affecting N successive samples of the signal y.sub.t, namely y.sub.t, y.sub.t-1, . . . , y.sub.t-N+1 and on the other hand a linear filtering of e.sub.t using the coefficients B1.sub.t, B2.sub.t, . . . , BP.sub.t of the conventional modeling of e.sub.t+1.
In other words, in the form of quantities: EQU A1.sub.t y.sub.t +A2.sub.t y.sub.t-1 + . . . +AN.sub.t y.sub.t-N+1 (i.e. py.sub.t) (10)
and EQU B1.sub.t e.sub.t +B2.sub.t e.sub.t + . . . +BP.sub.t e.sub.t-P+1 (i.e. pe.sub.t) (11)
In addition, these quantities are respectively weighted by two coefficients between 0 and 1 and not simultaneously zero, these coefficients being called .gamma..sub.AR (AR indicating autoregressive) and .gamma..sub.MA (the MA indicating adjusted mean).
Finally, the conventional predictions obtained on the restored values y.sub.t and e.sub.t, namely: EQU A1.sub.t y.sub.t +A2.sub.t y.sub.t-1 + . . . +AN.sub.t y.sub.t-N+1 (i.e. py.sub.t) (12)
and EQU B1.sub.t e.sub.t +B2.sub.t e.sub.t-1 + . . . +BP.sub.t e.sub.t-P+1 (i.e. pe.sub.t) (13)
are also weighted, by coefficients respectively equal to (1-.gamma..sub.AR) and (1-.gamma..sub.MA).
Thus, in summarizing according to the invention a prediction signal of form: EQU [(1-.gamma..sub.AR)py.sub.t +.gamma..sub.AR py.sub.t ]+[(1-.gamma..sub.MA)pe.sub.t +.gamma..sub.MA pe.sub.t ] (14)
is formed in which the quantities py.sub.t, py.sub.t, pe.sub.t and pe.sub.t are obtained by the summations defined by equations (10) to (13).
If the coefficients .gamma..sub.AR and .gamma..sub.MA were both zero, expression (14) would be reduced to py.sub.t +pe.sub.t, i.e. to the prediction according to the prior art. Conversely, if the coefficients are both equal to unity, expression (14) becomes py.sub.t +pe.sub.t (15).
These considerations only apply in the coding circuit where there are both restored signals y.sub.t and e.sub.t and real signals y.sub.t and e.sub.t. On decoding, there are naturally only restored signals y.sub.t and e.sub.t, so that only the two expressions according to (10) and (11) can be calculated, as in the prior art.
The interest of the specific modeling described hereinbefore with respect to the shaping of the quantization noise is as follows. At time t+1, the prediction error e.sub.t+1 is given by: EQU e.sub.t+1 =y.sub.t+1 -p.sub.t+1 ( 16)
in which the prediction p.sub.t+1 is given by the expression (14). It follows that the quantization noise on signal (.DELTA.y.sub.t+1 =y.sub.t+1 -y.sub.t+1) is linked with the quantization noise on the prediction error (.DELTA.e.sub.t+1 =e.sub.t+1 -e.sub.t+1) by the equation: EQU .DELTA.y.sub.t+1 =.gamma..sub.AR [A1.sub.t .DELTA.y.sub.t + . . . +AN.sub.t .DELTA.y.sub.t-N+1 ]+.gamma..sub.MA [B1.sub.t .DELTA.e.sub.t + . . . +BP.sub.t .DELTA.e.sub.t-P+1 ]+.DELTA.e.sub.t+1 ( 17)
On accepting that the quantization noise .DELTA.e.sub.t on the prediction error has an approximately flat spectrum, it follows that the spectrum of the quantization noise .DELTA.y.sub.t on the signal is given by the square of the module of the pole-zero transfer function equal to: ##EQU7## to that extent that it is assumes that the coefficients A1.sub.t, . . . ,AN.sub.t, B1.sub.t, . . . , BP.sub.t are relatively slowly readjusted so as to be considered as locally constant. If the constants A1, . . . AN, B1 . . . BP are adjusted so as to minimize the average power of the prediction error e.sub.t on the restored signal y.sub.t, the spectrum of the restored signal y.sub.t is obtained by taking the square of the module on the unity circle of the pole-zero transfer function equal to: ##EQU8##
For example, the choice of parameters .gamma..sub.AR =.gamma..sub.MA =1 gives the noise .DELTA.y.sub.t exactly the spectrum of the restored signal y.sub.t, which corresponds to a shaping by complete parallelism of the spectra, which is the sought objective. Conversely, the choice .gamma..sub.MA =.gamma..sub.AR =0 gives the noise .DELTA.y.sub.t the flat spectrum, which corresponds to the absence of shaping encountered in the prior art.
Naturally, between these two extreme situations, a random intermediate choice is possible leading to a shaping by approximate parallelism of the spectra of y.sub.t and .DELTA.y.sub.t. In particular, the choice .gamma..sub.AR =0, .gamma..sub.MA =1 makes it possible to obtain the zeros of the spectrum of y.sub.t in the spectrum of .DELTA.y.sub.t.
The tolerances on the parameters .gamma..sub.AR and .gamma..sub.MA therefore make it possible to carry out the spectral shaping in a random, very simple manner, whilst remaining relatively general. Thus, in this connection, the invention differs from the prior art methods in arriving at the said shaping.